Ubuntu Unleashed 2017 Edition (2017)

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MySQL

Tuning your MySQL server for increased performance is exceptionally easy to do, largely because you can see huge speed increases simply by getting your queries right. However, you can tune various things in the server itself to help it cope with higher loads as long as your system has enough RAM.

The key is understanding its buffers. There are buffers and caches for all sorts of things, and finding out how full they are is crucial to maximizing performance. MySQL performs best when it is making full use of its buffers, which in turn places a heavy demand on system RAM. Unless you have 4GB RAM or more in your machine, you do not have enough capacity to set very high values for all your buffers; you need to pick and choose.

Measuring Key Buffer Usage

When you add indexes to your data, it enables MySQL to find data faster. However, ideally you want to have these indexes stored in RAM for maximum speed, and the variable key_buffer_size defines how much RAM MySQL can allocate for index key caching. If MySQL cannot store its indexes in RAM, you will experience serious performance problems. Fortunately, most databases have relatively small key buffer requirements, but you should measure your usage to see what work needs to be done.

To do this, log in to MySQL and type SHOW STATUS LIKE '%key_read%'; that returns all the status fields that describe the hit rate of your key buffer. You should get two rows back: Key_reads and Key_read_requests, which are the number of keys being read from disk and the number of keys being read from the key buffer. From these two numbers you can calculate the percentage of requests being filled from RAM and from disk, using this simple equation:

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100 - ((Key_reads / Key_read_requests) ∞ 100)

That is, you divide Key_reads by Key_read_requests, multiply the result by 100, and then subtract the result from 100. For example, if you have Key_reads of 1000 and Key_read_requests of 100000, you divide 1000 by 100000 to get 0.01; then you multiply that by 100 to get 1.0, and subtract that from 100 to get 99. That number is the percentage of key reads being served from RAM, which means 99 percent of your keys are served from RAM.

Most people should be looking to get more than 95 percent served from RAM, although the primary exception is if you update or delete rows very often—MySQL can’t cache what keeps changing. If your site is largely read-only, this should be around 98 percent. Lower figures mean you might need to bump up the size of your key buffer.

If you are seeing problems, the next step is to check how much of your current key buffer is being used. Use the SHOW VARIABLES command and look up the value of the key_buffer_size variable. It is probably something like 8388600, which is 8 million bytes, or 8MB. Now, use the SHOW STATUS command and look up the value of Key_blocks_used.

You can determine how much of your key buffer is being used by multiplying Key_blocks_used by 1024, dividing by key_buffer_size, and multiplying by 100. For example, if Key_blocks_used is 8000, multiply that by 1024 to get 8192000; then divide that by your key_buffer_size (8388600) to get 0.97656, and finally multiply that by 100 to get 97.656. Thus, almost 98 percent of your key buffer is being used.

Now, the important part: You have ascertained that you are reading lots of keys from disk, and you also know that the reason for reading from disk is almost certainly because you do not have enough RAM allocated to the key buffer. A general rule is to allocate as much RAM to the key buffer as you can, up to a maximum of 25 percent of system RAM—128MB on a 512MB system is about the ideal for systems that read heavily from keys. Beyond that, you will actually see drastic performance decreases because the system has to use virtual memory for the key buffer.

Open /etc/my.cnf in your text editor and look for the line that contains key_buffer_size. If you do not have one, you need to create a new one. It should be under the line [mysqld]. When you set the new value, do not just pick some arbitrarily high number. Try doubling what is there right now (or try 16MB if there’s no line already); then see how it goes. To set 16MB as the key buffer size, you need a line like this:

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[mysqld]
set-variable = key_buffer_size=16M
datadir=/var/lib/mysql

Restart your MySQL server with service mysqld restart and then go back into MySQL and run SHOW VARIABLES again to see the key_buffer_size. It should be 16773120 if you have set it to 16M. Now, because MySQL just got reset, all its values for key hits and the like will also have been reset. You need to let it run for a while so that you can assess how much has changed. If you have a test system you can run, this is the time to run it.

After your database has been accessed with normal usage for a short while (if you get frequent accesses, this might be only a few minutes), recalculate how much of the key buffer is being used. If you get another high score, double the size again, restart, and retest. You should keep repeating this until your key buffer usage is below 50 percent or you find you don’t have enough RAM to increase the buffer further. Remember that you should never allocate more than 25 percent of system RAM to the key buffer.

Using the Query Cache

Newer versions of MySQL allow you to cache the results of queries so that, if new queries come in that use the same SQL, the result can be served from RAM. In some ways the query cache is quite intelligent: If, for example, part of the result changes due to another query, the cached results are thrown away and recalculated next time. However, in other ways it is very simple. For example, it uses cached results only if the new query is exactly the same as a cached query, even down to the capitalization of the SQL.

The query cache works well in most scenarios. If your site has an equal mix of reading and writing, the query cache does its best but is not optimal. If your site is mostly reading with few writes, more queries are cached (and for longer), thus improving overall performance.

First, you need to find out whether you have the query cache enabled. To do this, use SHOW VARIABLES and look up the value of have_query_cache. All being well, you should get YES back, meaning that the query cache is enabled. Next, look for the value of query_cache_size and query_cache_limit. The first is how much RAM in bytes is allocated to the query cache, and the second is the maximum result size that should be cached. A good starting set of values for these two is 8388608 (8MB) and 1048576 (1MB).

Next, type SHOW STATUS LIKE 'qcache%'; to see all the status information about the query cache. You should get output like this:

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mysql> SHOW STATUS LIKE 'qcache%';
+-------------------------+--------+
| Variable_name | Value |
+-------------------------+--------+
| Qcache_free_blocks | 1 |
| Qcache_free_memory | 169544 |
| Qcache_hits | 698 |
| Qcache_inserts | 38 |
| Qcache_lowmem_prunes | 20 |
| Qcache_not_cached | 0 |
| Qcache_queries_in_cache | 18 |
| Qcache_total_blocks | 57 |
+-------------------------+--------+
8 rows in set (0.00 sec)

From that, we can see that only 18 queries are in the cache (Qcache_queries_in_cache); we have 169544 bytes of memory free in the cache (Qcache_free_memory), 698 queries have been read from the cache (Qcache_hits), 38 queries have been inserted into the cache (Qcache_inserts), but 20 of them were removed due to lack of memory (Qcache_lowmem_prunes), giving the 18 from before. Qcache_not_cached is 0, which means 0 queries were not cached—MySQL is caching them all.

From that, we can calculate how many total queries came in—it is the sum of Qcache_hits, Qcache_inserts, and Qcache_not_cached, which is 736. We can also calculate how well the query cache is being used by dividing Qcache_hits by that number and multiplying by 100. In this case, 94.84 percent of all queries are being served from the query cache, which is a great number.

In our example, we can see that many queries have been trimmed because there is not enough memory in the query cache. This can be changed by editing your /etc/my.cnf file and adding a line like this one, somewhere in the [mysqld] section:

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set-variable = query_cache_size=32M.

An 8MB query cache should be enough for most people, but larger sites might need 16MB or even 32MB if you are storing a particularly large amount of data. Very few sites will need to go beyond a 32MB query cache, but keep an eye on the Qcache_lowmem_prunes value to ensure you have enough RAM allocated.

Using the query cache does not incur much of a performance hit. When MySQL calculates the result of a query normally, it throws it away when the connection closes. With the query cache, it skips the throwing away, and so there is no extra work being done. If your site does have many updates and deletes, be sure to check whether you get any speed boost at all from the query cache.

Miscellaneous Tweaks

If you have tuned your key buffer and optimized your query cache and yet still find your site struggling, you can make a handful of smaller changes that will add some more speed.

When reading from tables, MySQL has to open the file that stores the table data. How many files it keeps open at a time is defined by the table_cache setting, which is set to 64 by default. You can increase this setting if you have more than 64 tables, but be aware that Ubuntu imposes limits on MySQL about how many files it can have open at a time. Going beyond 256 is not recommended unless you have a particularly database-heavy site and know exactly what you are doing.

The other thing you can tweak is the size of the read buffer, which is controlled by read_buffer_size and read_buffer_rnd_size. Both of these are allocated per connection, which means you should be very careful to have large numbers. Whatever you choose, read_buffer_rnd_size should be three to four times the size of read_buffer_size, so if read_buffer_size is 1MB (suitable for very large databases), read_buffer_rnd_size should be 4MB.

Query Optimization

The biggest speed-ups can be seen by reprogramming your SQL statements so they are more efficient. If you follow these tips, your server will thank you:

Select as little data as possible. Rather than SELECT *, select only the fields you need.

If you need only a few rows, use LIMIT to select the number you need.

Declare fields as NOT NULL when creating tables to save space and increase speed.

Provide default values for fields and use them where you can.

Be very careful with table joins because they are the easiest way to write inefficient queries.

If you must use joins, be sure you join on fields that are indexed. They should also preferably be integer fields because these are faster than strings for comparisons.

Find and fix slow queries. Add log-long-format and log-slow-queries = /var/log/slow-queries.log to your /etc/my.cnf file, under [mysqld], and MySQL tells you the queries that took a long time to complete.

Use OPTIMIZE TABLE tablename to defragment tables and refresh the indexes.

References

www.coker.com.au/bonnie++/—The home page of bonnie, a disk benchmarking tool. It also contains a link to RAID benchmarking utilities and Postal, a benchmarking utility for SMTP servers.

www.phoronix-test-suite.com/—The Phoronix Test Suite was created by a website that does automated performance testing and comparisons and is a quality benchmarking software to consider.

http://httpd.apache.org/current/misc/perf-tuning.html—The official Apache guide to tuning your web server.

http://dev.mysql.com/doc/refman/5.7/en/optimization.html—Learn how to optimize your MySQL server direct from the source, the MySQL manual.

One particular MySQL optimization book will really help you get more from your system if you run a large site: High Performance MySQL, by Jeremy Zawodny and Derek Balling (O’Reilly), ISBN: 0-596-00306-4.